Soft Peristaltic Actuation for the Harvesting of Ovine Offal

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 345)


Many tasks in lamb meat processing have been automated by mechatronic systems during the past years. However, the extraction of edible organs from the unordered organ package has remained a challenge. Traditional sensing methods and hard robotic effectors are not suitable for the slippery and deformable tissue in varying geometric constellations. In this paper, we propose a soft peristaltic method to bring the organ package into the optimal configuration for the removal of single organs. We give a system overview, discuss its viability, and point out the challenges in its implementation.

A deformable xy-sorting table is proposed to order the organ package. By producing moving wave shapes on its surface, the table changes the geometric configuration of the organs as perceived and controlled by a machine vision module. When an organ is in the optimal position, it is picked up and removed by traditional robotic solutions.


Soft robotics peristalsis meat processing 


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  1. 1.
    “MIA, Annual Report,” Meat Industry Association MIA (Trade Association representing New Zealand meat processors, exporters and marketers) (2013)Google Scholar
  2. 2.
    Meat Technology Update, CSIRO Food and Nutritional Sciences: Meat Industry Services (June 2008)Google Scholar
  3. 3.
    Choi, S., Zhang, G., Fuhlbrigge, T., Watson, T., Tallian, R.: Applications and Requirements of Industrial Robots in Meat Processing. In: International Conference on Automation Science and Engineering (CASE), vol. 2, pp. 1107–1112. IEEE (2013)Google Scholar
  4. 4.
    Jansen, T.C., Spijker, R.: Method and apparatus for mechanically processing an organ or organs taken out from slaughtered poultry. U.S. Patent No. 20110237171 A1 (2011)Google Scholar
  5. 5.
    Furst, J.D., Susomboom, R., Raicu, D.S.: Single organ segmentation filters for multiple organ segmentation. In: Annual International IEEE Conference on Engineering in Medicine and Biology Society (EMBS), pp. 3033–3036 (2006)Google Scholar
  6. 6.
    Pauly, O., Glocker, B., Criminisi, A., Mateus, D., Möller, A.M., Nekolla, S., Navab, N.: Fast Multiple Organ Detection and Localization in Whole-Body MR Dixon Sequences. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 239–247. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Venkatraghavan, V., Ranjan, S.: Generic Framework for Organ Localization in CT and MR Images. In: Applied Imagery Pattern Recognition Workshop (AIPR). IEEE (2011)Google Scholar
  8. 8.
    Kim, S., Laschi, C., Trimmer, B.A.: Soft Robotics: a Bioinspired Evolution in Robotics. Trends in Biotechnology 31, 287–294 (2013)CrossRefGoogle Scholar
  9. 9.
    Trivedi, D., Rahn, C.D., Kier, W.M., Walker, I.D.: Soft robotics: Biological inspiration, state of the art, and future research. Applied Bionics and Biomechanics 5, 99–117 (2008)CrossRefGoogle Scholar
  10. 10.
    Shepherd, R.F., Stokes, A.A., Nunes, R.M.D., Whitesides, G.M.: Soft Machines That are Resistant to Puncture, and That Self Seal. Advanced Materials 25(46), 6709–6713 (1998)CrossRefGoogle Scholar
  11. 11.
    Stokes, A.A., Shepherd, R.F., Morin, S.A., Ilievski, F., Whitesides, G.M.: A Hybrid Combining Hard and Soft Robots. Soft Robotics 1 (2013)Google Scholar
  12. 12.
    Chen, F.J., Dirven, S., Xu, W.L., Li, X.N.: Soft Actuator Mimicking Human Esophageal Peristalsis for a Swallowing Robot. IEEE/ASME Transactions on Mechatronics (2013)Google Scholar
  13. 13.
    Dirven, S., Chen, F.J., Xu, W.L., Bronlund, J.E., Allen, J., Cheng, L.K.: Design and Characterization of a Peristaltic Actuator Inspired by Esophageal Swallowing. IEEE/ASME Transactions on Mechatronics (2013)Google Scholar
  14. 14.
    Lin, H.-T., Leisk, G.G., Trimmer, B.: GoQBot: A Caterpillar-inspired Soft-bodied Rolling Robot. Bioinspiration & Biomimetics 6, 026007 (2011)CrossRefGoogle Scholar
  15. 15.
    Shepherd, R.F., Ilievski, F., Choi, W., Morin, S.A., Stokes, A.A., Mazzeo, A.D., Chen, X., Wang, M., Whitesides, G.M.: Multigait soft robot. Proceedings of the National Academy of Sciences, PNAS (2011)Google Scholar
  16. 16.
    Shepherd, R.F., Stokes, A.A., Freake, J., Barber, J., Snyder, P.W., Mazzeo, A.D., Cademartiri, L., Morin, S.A., Whitesides, G.M.: Using Explosions to Power a Soft Robot. Angewandte Chemie International Edition 52, 2892–2896 (2013)CrossRefGoogle Scholar
  17. 17.
    Laschi, C., Cianchetti, M., Mazzolai, B., Margheri, L., Follador, M., Dario, P.: Soft Robot Arm Inspired by the Octopus. Advanced Robotics 26, 709–727 (2012)CrossRefGoogle Scholar
  18. 18.
    Brown, E., Rodenberg, N., Amend, J., Mozeika, A., Steltz, E., Zakin, M., Lipson, H., Jaeger, H.: Universal robotic gripper based on the jamming of granular material. Proceedings of the National Academy of Sciences (PNAS) 107(44), 18809–18814 (2010)CrossRefGoogle Scholar
  19. 19.
    Kang, R., Branson, D.T., Caldwell, E.G.D.G.: Dynamic Modeling and Control of an Octopus Inspired Multiple Continuum Arm Robot. Computers & Mathematics with Applications 64, 1004–1016 (2012)CrossRefGoogle Scholar
  20. 20.
    Brochu, P., Pei, Q.: Advances in Dielectric Elastomers for Actuators and Artificial Muscles. Macromolecular Rapid Communications 31, 10–36 (2010)CrossRefGoogle Scholar
  21. 21.
    Carpi, F., Frediani, G., Turco, S., Rossi, D.D.: Bioinspired Tunable Lens with Muscle-Like Electroactive Elastomers. Advanced Functional Materials 21, 4152–4158 (2011)CrossRefGoogle Scholar
  22. 22.
    Wong, R.D.P., Posnerb, J.D., Santos, V.J.: Flexible microfluidic normal force sensor skin for tactile feedback. Sensors and Actuators A: Physical 179, 62–69 (2012)CrossRefGoogle Scholar
  23. 23.
    Park, Y.L., Chen, B.R., Wood, R.J.: Design and Fabrication of Soft Artificial Skin Using Embedded Microchannels and Liquid Conductors. IEEE Sensors Journal 12, 2711–2718 (2012)CrossRefGoogle Scholar
  24. 24.
    Lowe, D.G.: Object Recognition from Local Scale-Invariant Features. In: International Converence on Computer Vision (ICCV), pp. 1150–1157 (1999)Google Scholar
  25. 25.
    Ojala, T., Pietikäinen, M., Harwood, D.: Performance evaluation of texture measures with classification based on kullback discrimination of distributions. In: IAPR International Conference on Pattern Recognition (ICPR), pp. 582–585 (1994)Google Scholar
  26. 26.
    Stommel, M., Beetz, M.: Sampling and Clustering of the Space of Human Poses from Tracked, Skeletonised Colour+Depth Images. Technical Report 70, Center for Computing and Communication Technologies, University of Bremen, Germany (2013)Google Scholar
  27. 27.
    Zhu, M.Z., Xu, W.L., Bronlund, J.: CPG-based control of a swallowing robot. International Journal of Computer Applications in Technology (2013)Google Scholar
  28. 28.
    Matsuoka, K.: Sustained Oscillations Generated by Mutually Inhibiting Neurons with Adaptation. Biological Cybernetics 52, 367–376 (1985)CrossRefMATHMathSciNetGoogle Scholar
  29. 29.
    Martinez, R.V., Branch, J.L., Fish, C.R., Lin, L., Suo, Z., Whitesides, G.M.: Robotic Tentacles with Three-Dimensional Mobility Based on Flexible Elastomers. Advanced Materials 25, 205–212 (2013)CrossRefGoogle Scholar
  30. 30.
    Singh, J., Potgieter, J., Xu, W.L.: Ovine automation: robotic brisket cutting. Industrial Robot: An International Journal 39(2), 191–196 (2012)CrossRefGoogle Scholar
  31. 31.
    Xia, Y.N., Whitesides, G.M.: Soft lithography. Angewandte Chemie International Edition 37(5), 550–575 (1998)CrossRefGoogle Scholar
  32. 32.
    Cuadra, M.B.: Atlas-based segmentation and classification of magnetic resonance brain images. PhD thesis, Ecole Polytechnique Federale De Lausanne (2003)Google Scholar
  33. 33.
    Glocker, B., Pauly, O., Konukoglu, E., Criminisi, A.: Joint Classification-Regression Forests for Spatially Structured Multi-Object Segmentation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 870–881. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  34. 34.
    Liu, X., Song, Q., Mendonca, P., Tao, X., Bhotika, R.: Organ Labeling Using Anatomical Model-Driven Global Optimization. In: IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology (HISB), pp. 338–345 (2011)Google Scholar
  35. 35.
    Bay, H., Ess, A., Tuytelaars, T., van Gool, L.: SURF: Speeded Up Robust Features. Computer Vision and Image Understanding (CVIU) 110(3), 346–359 (2006)CrossRefGoogle Scholar
  36. 36.
    Ke, Y., Sukthankar, R.: PCA-SIFT: A More Distinctive Representation for Local Image Descriptors. In: Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 506–513. IEEE (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • M. Stommel
    • 1
  • W. L. Xu
    • 2
  • P. P. K. Lim
    • 3
  • B. Kadmiry
    • 3
  1. 1.Department of Electrical and Electronics EngineeringAuckland University of TechnologyAucklandNew Zealand
  2. 2.Department of Mechanical EngineeringThe University of AucklandAucklandNew Zealand
  3. 3.Callaghan InnovationAucklandNew Zealand

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